{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2a7a9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用逻辑回归算法对鸢尾花数据集（或其他数据集）建模进行分类预测\n",
    "\n",
    "# 按照序号1-6，完成要求\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21587a5a",
   "metadata": {},
   "source": [
    "###  导入数据（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02518d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "X = load_iris().data\n",
    "Y = load_iris().target\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "574f4bec",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>4.9</td>\n",
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       "       0    1    2    3\n",
       "0    5.1  3.5  1.4  0.2\n",
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       "2    4.7  3.2  1.3  0.2\n",
       "3    4.6  3.1  1.5  0.2\n",
       "4    5.0  3.6  1.4  0.2\n",
       "..   ...  ...  ...  ...\n",
       "145  6.7  3.0  5.2  2.3\n",
       "146  6.3  2.5  5.0  1.9\n",
       "147  6.5  3.0  5.2  2.0\n",
       "148  6.2  3.4  5.4  2.3\n",
       "149  5.9  3.0  5.1  1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(X)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "988c76ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54a6ca54",
   "metadata": {},
   "outputs": [
    {
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       "      <td>5.9</td>\n",
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       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
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      ],
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       "       0    1    2    3  label\n",
       "0    5.1  3.5  1.4  0.2      0\n",
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       "2    4.7  3.2  1.3  0.2      0\n",
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       "4    5.0  3.6  1.4  0.2      0\n",
       "..   ...  ...  ...  ...    ...\n",
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       "146  6.3  2.5  5.0  1.9      2\n",
       "147  6.5  3.0  5.2  2.0      2\n",
       "148  6.2  3.4  5.4  2.3      2\n",
       "149  5.9  3.0  5.1  1.8      2\n",
       "\n",
       "[150 rows x 5 columns]"
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2af9b3",
   "metadata": {},
   "source": [
    "### 切分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "30b3ee2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X, Y, test_size=0.3,random_state=420 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a46db0f",
   "metadata": {},
   "source": [
    "### 使用标准化包，学习训练集的数据分布，从而对训练集和测试集来做标准化（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "62cf5800",
   "metadata": {},
   "outputs": [],
   "source": [
    "#对训练集和测试集做标准化---去量纲\n",
    "std = StandardScaler().fit(Xtrain)\n",
    "Xtrain_ = std.transform(Xtrain)\n",
    "Xtest_ = std.transform(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83035c7",
   "metadata": {},
   "source": [
    "### 在确定l2范式的情况下，使用网格搜索判断solver, C的最优组合（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e154841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(max_iter=10000),\n",
       "             param_grid={'C': [0.05, 0.10277777777777777, 0.15555555555555556,\n",
       "                               0.20833333333333331, 0.2611111111111111,\n",
       "                               0.3138888888888889, 0.36666666666666664,\n",
       "                               0.41944444444444445, 0.4722222222222222, 0.525,\n",
       "                               0.5777777777777778, 0.6305555555555556,\n",
       "                               0.6833333333333333, 0.7361111111111112,\n",
       "                               0.788888888888889, 0.8416666666666667,\n",
       "                               0.8944444444444445, 0.9472222222222223, 1.0],\n",
       "                         'solver': ['liblinear', 'sag', 'newton-cg', 'lbfgs']})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#在l2范式下，判断C和solver的最优值\n",
    "p = {\n",
    "    'C':list(np.linspace(0.05,1,19)),\n",
    "    'solver':['liblinear','sag','newton-cg','lbfgs']\n",
    "}\n",
    "\n",
    "model = LogisticRegression(penalty='l2',max_iter=10000)\n",
    "\n",
    "GS = GridSearchCV(model,p,cv=5)\n",
    "GS.fit(Xtrain_,Ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0b1da1",
   "metadata": {},
   "source": [
    "### 将最优的结果重新用来实例化模型，查看训练集和测试集下的分数（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ea08162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714285714285715"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "GS.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d510f753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.41944444444444445, 'solver': 'sag'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1b7fdd33",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression(penalty='l2',\n",
    "           max_iter=10000,\n",
    "           C=GS.best_params_['C'],\n",
    "           solver=GS.best_params_['solver']\n",
    "          ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1fb63054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.41944444444444445, max_iter=10000, solver='sag')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain_,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "28874b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9714285714285714, 0.9555555555555556)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(Xtrain_,Ytrain),model.score(Xtest_,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dab7414f",
   "metadata": {},
   "source": [
    "### 计算精准率（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0be023b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "057a4cb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9714285714285714"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(注意多分类需要增加参数  average='micro')\n",
    "y_pred = model.predict(Xtrain_)\n",
    "precision_score(Ytrain, y_pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1eab8620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9555555555555556"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest_pred = model.predict(Xtest_)\n",
    "precision_score(Ytest, ytest_pred, average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "281e58bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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